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Register a new userExplaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
شرح تنبؤات الشبكة العصبية في أزواج الجملة عبر أقنعة تعلم مجموعة الكلمات
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شرح نماذج الشبكة العصبية مهمة لزيادة جدورث بالثقة في تطبيقات العالم الحقيقي. توليد معظم الأساليب الموجودة تفسيرات ما بعد الهخص لنماذج الشبكة العصبية من خلال تحديد وسمات الميزات الفردية أو الكشف عن التفاعلات بين الميزات المجاورة. ومع ذلك، بالنسبة للنماذج التي تحتوي على أزواج نصية كدخلات (على سبيل المثال، التعريف بإعادة صياغة التعريف)، فإن الأساليب الموجودة ليست كافية لالتقاط تفاعلات الميزات بين نصين وتمديدها البسيط لحساب جميع تفاعلات Word-Beach بين نصفي غير فعال. في هذا العمل، نقترح طريقة Group Mask (GMASK) لتكتشف ضمنيا علاقات الكلمات من خلال تجميع الكلمات المرتبطة من زوج إدخال الإدخال معا وقياس مساهمتها في مهام NLP المقابلة ككل. يتم تقييم الطريقة المقترحة مع اثنين من بنية نموذجية مختلفة (نموذج الاهتمام للتحلل) عبر أربع مجموعات بيانات، بما في ذلك الاستدلال اللغوي الطبيعي وإعادة صياغة مهام التعريف. تظهر التجارب فعالية Gmask في توفير تفسيرات مخلصة لهذه النماذج.
Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.
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